Deep latent variable models have been shown to facilitate the response
generation for open-domain dialog systems. However, these latent variables are
highly randomized, leading to uncontrollable generated responses...
paper, we propose a framework allowing conditional response generation based on
specific attributes. These attributes can be either manually assigned or
automatically detected. Moreover, the dialog states for both speakers are
modeled separately in order to reflect personal features. We validate this
framework on two different scenarios, where the attribute refers to genericness
and sentiment states respectively. The experiment result testified the
potential of our model, where meaningful responses can be generated in
accordance with the specified attributes.